The desire to use a graphics processing unit (GPU) for general computation has become much more pronounced recently due to the GPU's exemplary performance per unit power and/or cost. The computational capabilities for GPUs, generally, have grown at a rate exceeding that of the corresponding central processing unit (CPU) platforms. This growth, coupled with the explosion of the mobile computing market and its necessary supporting server/enterprise systems, has been used to provide a specified quality of desired user experience. Consequently, the combined use of CPUs and GPUs for executing workloads with data parallel content is becoming a volume technology.
However, GPUs have traditionally operated in a constrained programming environment, available only for the acceleration of graphics. These constraints arose from the fact that GPUs did not have as rich a programming ecosystem as CPUs. Their use, therefore, has been mostly limited to two dimensional (2D) and three dimensional (3D) graphics and a few leading edge multimedia applications, which are already accustomed to dealing with graphics and video application programming interfaces (APIs).
With the advent of multi-vendor supported OpenCL® and DirectCompute®, standard APIs and supporting tools, the limitations of the GPUs in traditional applications has been extended beyond traditional graphics. Although OpenCL and DirectCompute are a promising start, there are many hurdles remaining to creating an environment and ecosystem that allows the combination of the CPU and GPU to be used as fluidly as the CPU for most programming tasks.
Existing computing systems often include multiple processing devices. For example, some computing systems include both a CPU and a GPU on separate chips (e.g., the CPU might be located on a motherboard and the GPU might be located on a graphics card) or in a single chip package. Both of these arrangements, however, still include significant challenges associated with (i) separate memory systems, (ii) efficient scheduling, (iii) providing quality of service (QoS) guarantees between processes, (iv) programming model, and (v) compiling to multiple target instruction set architectures (ISAs)—all while minimizing power consumption.
For example, the discrete chip arrangement forces system and software architects to utilize chip to chip interfaces for each processor to access memory. While these external interfaces (e.g., chip to chip) negatively affect memory latency and power consumption for cooperating heterogeneous processors, the separate memory systems (i.e., separate address spaces) and driver managed shared memory create overhead that becomes unacceptable for fine grain offload.
Both the discrete and single chip arrangements can limit the types of commands that can be sent to the GPU for execution. By way of example, computational commands (e.g., physics or artificial intelligence commands) often cannot be sent to the GPU for execution. This limitation exists because the CPU may relatively quickly require the results of the operations performed by these computational commands. However, because of the high overhead of dispatching work to the GPU in current systems and the fact that these commands may have to wait in line for other previously-issued commands to be executed first, the latency incurred by sending computational commands to the GPU is often unacceptable.
Given that a traditional GPU may not efficiently execute some computational commands, the commands must then be executed within the CPU. Having to execute the commands on the CPU increases the processing burden on the CPU and can hamper overall system performance.
Although GPUs provide excellent opportunities for computational offloading, traditional GPUs may not be suitable for system-software-driven process management that is desired for efficient operation in some multi-processor environments. These limitations can create several problems.
For example, since processes cannot be efficiently identified and/or preempted, a rogue process can occupy the GPU hardware for arbitrary amounts of time. In other cases, the ability to context switch off the hardware is severely constrained—occurring at very coarse granularity and only at a very limited set of points in a program's execution. This constraint exists because saving the necessary architectural and microarchitectural states for restoring and resuming a process is not supported. Lack of support for precise exceptions prevents a faulted job from being context switched out and restored at a later point, resulting in lower hardware usage as the faulted threads occupy hardware resources and sit idle during fault handling.
Arbitration occurs at two different levels within a computer system. One level relates to what job is being driven at the front end of the GPU compute pipeline. The other level relates to utilization of shared resources. Because there are multiple tasks being executed simultaneously, these tasks must be prioritized. Therefore, a decision is required to determine how shared resources will be utilized. For example, how will tasks be prioritized as they arrive at the beginning of the dispatch pipeline and travel to the shader core.